Builds LLM systems that hold up in production, including retrieval, tool use, orchestration, caching, batching, evaluation harnesses, and human-in-the-loop controls, with genuine attention to accuracy, latency, spend, safety, and auditability.
Drives team adoption of enterprise-authorized AI-assisted engineering practices to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation; sound delivery automation and infrastructure-as-code are simply expected.
Champions responsible AI use by setting expectations for validating AI outputs for correctness, performance, and security, with attention to data sensitivity, secure handling of inputs and outputs, and adherence to resiliency and security, and coaches engineers on safe, compliant adoption.
Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture.
Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems.
Provides technical leadership on data projects across engineers, product managers, and stakeholders, and provides mentorship and guidance to junior engineers, leaving systems others can confidently own.
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and advanced applied experience.
Hands-on practical experience delivering system design, application development, testing, and operational stability.
Demonstrable strength in a cloud-based, cloud-native engineering environment.
Advanced Python, with additional programming languages such as Java, Kotlin, or Scala a plus, and the range to work across more than one.
Deep distributed-systems fundamentals: partitioning, parallelism, state, fault tolerance, and performance tuning under real load.
Production experience across data engineering and streaming platforms at meaningful volume.
Practical ML engineering across inference, evaluation, monitoring, and deployment, not just model training.
Real LLM delivery beyond prototypes, with a clear view of quality, cost, safety, and governance.
Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting), with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices.
Proficient in all aspects of the Software Development Life Cycle, with a good understanding of databases and data structures.
Advanced understanding of agile methodologies such as CI/CD, application resiliency, and security.
A proven record of leading technical delivery across teams while contributing directly to the code, with clear, pragmatic, evidence-driven communication.
In-depth knowledge of the financial services industry and its IT systems, and practical cloud-native experience; regulated or high-governance environments, agent frameworks, MCP, RAG, and vector search, Kubernetes and modern observability, and real-time analytics or search-centric architectures are a plus.
